https://github.com/the-developer-306/contextual_ai_presentation_orchestrator
https://github.com/the-developer-306/contextual_ai_presentation_orchestrator
Last synced: 8 months ago
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- Host: GitHub
- URL: https://github.com/the-developer-306/contextual_ai_presentation_orchestrator
- Owner: the-developer-306
- Created: 2025-09-06T11:08:22.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-09-13T11:40:10.000Z (9 months ago)
- Last Synced: 2025-09-13T12:27:59.657Z (9 months ago)
- Language: Python
- Size: 394 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Contextual AI Presentation Orchestrator
=======================================
Overview
--------
The Contextual AI Presentation Orchestrator is a multi-agent system designed to automatically transform raw documents and topics into professional, structured PowerPoint presentations. It leverages Retrieval-Augmented Generation (RAG), specialized content agents, and role-based access control (RBAC) to ensure accurate, fact-checked, and presentation-ready outputs.
This project demonstrates a production-grade AI system with end-to-end functionality, from ingestion to orchestration, validation, and final PPTX generation.
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Key Features
------------
- **Multi-Agent Orchestration**: Five specialized agents (Document Analysis, Outline Generator, Content Expansion, QA, and Format Optimizer).
- **RAG Pipeline**: FAISS/ChromaDB-based vector store with hybrid retrieval, citation tracking, and contradiction detection.
- **Context Management**: Memory modules and session persistence for multi-turn workflows.
- **Security**: JWT authentication and RBAC for four roles (Executive, Senior Manager, Analyst, Junior Staff).
- **Deployment**: Fully containerized using Docker and orchestrated with Docker Compose.
- **API + CLI + Frontend**: REST endpoints via FastAPI, CLI via Typer, and user interface via Streamlit.
- **Production Readiness**: Logging, observability, and pytest-based validation.
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System Architecture Diagram
---------------------------
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Development Workflow
--------------------
**Phase 1 -- Architecture & Planning**
- Defined multi-agent orchestration, RAG pipeline, memory, and security model.
**Phase 2 -- Core Implementation**
- Built preprocessing and hybrid retrieval.
- Developed 5 custom agents and integrated via LangChain.
- Exposed via FastAPI and CLI.
**Phase 3 -- Production Readiness**
- Implemented JWT-based authentication and RBAC.
- Added observability and pytest coverage.
- Dockerized backend and frontend for containerized deployment.
**Phase 4 -- Final Polish**
- Executed end-to-end testing.
- Prepared system diagram, documentation, and demo.
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Future Enhancements
-------------------
- **Professional PPT Design**: Integrate advanced design hints into python-pptx for visually consistent, branded presentations.
- **Real-Time Multi-User Collaboration**: Enable live co-editing of outlines and expansions through WebSocket/Socket.io integration.
- **Database-Backed Authentication**: Migrate from hardcoded role mapping to a persistent database-driven authentication and role management system (e.g., PostgreSQL).
- **Scalability**: Deploy on Kubernetes with horizontal scaling of orchestrator pods.
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Tech Stack
----------
- **Core**: Python 3.11
- **LLM Orchestration**: LangChain + ChatGroq
- **Vector Search**: FAISS / ChromaDB
- **Embeddings**: Sentence Transformers (all-MiniLM-L6-v2)
- **API Framework**: FastAPI
- **Frontend**: Streamlit
- **Auth**: JWT with RBAC
- **PPT Generation**: python-pptx
- **Deployment**: Docker + Docker Compose
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Developer Setup Guide
=====================
This guide explains how to set up, run, and test the **Contextual AI Presentation Orchestrator** locally and via Docker.
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1\. Prerequisites
-----------------
- Python 3.11+
- Docker & Docker Compose
- Git
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2\. Clone the Repository
------------------------
`git clone https://github.com/yourusername/contextual_ai_presentation_orchestrator.git
cd contextual_ai_presentation_orchestrator`
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3\. Environment Setup (Local)
-----------------------------
### Create Virtual Environment
`python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate`
### Install Dependencies
`pip install --upgrade pip
pip install -r requirements.txt`
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4\. Running Backend (Local)
---------------------------
Navigate to the **api** folder:
`cd api
uvicorn main:app --reload --port 8000`
Backend will start at:
`http://127.0.0.1:8000`
Docs available at:
`http://127.0.0.1:8000/docs`
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5\. Running Frontend (Local)
----------------------------
Go back to root and run Streamlit app:
`cd ..
streamlit run frontend/app.py --server.port=8501`
Frontend available at:
`http://127.0.0.1:8501`
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6\. Docker Setup (Recommended)
------------------------------
### Build and Run Containers
From project root:
`docker-compose build
docker-compose up`
- Frontend runs on ****
- Backend runs on ****
Logs for each service:
`docker-compose logs backend
docker-compose logs frontend`
### Stop Containers
`docker-compose down`
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7\. Project Structure
---------------------
contextual_ai_presentation_orchestrator/
│── agents/ # Multi-agent orchestration logic
│ ├── orchestration.py
│ ├── document_analysis_agent.py
│ ├── outline_generator_agent.py
│ ├── content_expansion_agent.py
│ ├── qa_agent.py
│ └── format_optimizer_agent.py
│
│── rag_pipeline/ # RAG (Retrieval-Augmented Generation) pipeline
│ ├── preprocess.py
│ ├── embeddings.py
│ ├── vector_store.py
│ └── retriever.py
│
│── api/ # Backend (FastAPI)
│ ├── routes/
│ │ ├── memory.py
│ │ ├── auth.py
│ │ ├── upload.py
│ │ ├── generate.py
│ │ └── download.py
│ └── main.py
│
│── cli/ # CLI interface
│ └── cli_interface.py
│
│── utils/ # Utilities (security, PPTX, logging, config, etc.)
│ ├── security.py
│ ├── ppt_generator.py
│ ├── middleware.py
│ ├── memory.py
│ ├── config.py
│ └── logger.py
│
│── tests/ # Pytest unit tests
│── Dockerfile # Backend Dockerfile
│── Dockerfile.frontend # Frontend Dockerfile
│── docker-compose.yml # Orchestration of backend + frontend
│── requirements.txt # Dependencies
│── README.md # Documentation
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